5 papers · 1 filter
Geometry-Aware Bayesian Quantification via Compositional Data Analysis
Alejandro Moreo, Pablo González, Juan José del Coz
Accurately estimating the unknown target label distribution is the critical first step for adapting to label shift. This task, widely known as quantification or class prevalence es…
Efficient quantification on large-scale networks
Alessio Micheli, Alejandro Moreo, Marco Podda +3
Network quantification (NQ) is the problem of estimating the proportions of nodes belonging to each class in subsets of unlabelled graph nodes. When prior probability shift is at p…
Transductive Model Selection under Prior Probability Shift
Lorenzo Volpi, Alejandro Moreo, Fabrizio Sebastiani
Transductive learning is a supervised machine learning task in which, unlike in traditional inductive learning, the unlabelled data that require labelling are a finite set and are…
On the Interconnections of Calibration, Quantification, and Classifier Accuracy Prediction under Dataset Shift
Alejandro Moreo
When the distribution of the data used to train a classifier differs from that of the test data, i.e., under dataset shift, well-established routines for calibrating the decision s…
Forging the Forger: An Attempt to Improve Authorship Verification via Data Augmentation
Silvia Corbara, Alejandro Moreo
Authorship Verification (AV) is a text classification task concerned with inferring whether a candidate text has been written by one specific author or by someone else. It has been…